Linear Optimal Partial Transport Embedding

arXiv:2302.03232v4 Announce Type: replace
Abstract: Optimal transport (OT) has gained popularity due to its various applications in fields such as machine learning, statistics, and signal processing. However, the balanced mass requirement limits its performance in practical problems. To address these limitations, variants of the OT problem, including unbalanced OT, Optimal partial transport (OPT), and Hellinger Kantorovich (HK), have been proposed. In this paper, we propose the Linear optimal partial transport (LOPT) embedding, which extends the (local) linearization technique on OT …

abstract applications arxiv cs.lg embedding fields however limitations linear machine machine learning math.oc performance practical processing signal statistics transport type variants